Neural networks are often employed as predictors in situations where 
knowledge of the system dynamics are largely unknown \citep{lundegaard2008netmhc, nielsen2009nn}. If multiple networks are trained on a single dataset, by varying the parameters of the
networks, it is often found that no single network consistently outperforms
the others. It has empirically been found that ensembles of many networks,
where the final prediction is the mean of multiple predictions tend to outperform
single networks \citep{hansen1990neural, nielsen2003reliable}, and such ensembles are often used \citep{zhou2002lung, fernandez2005modeling}. However in modern studies
the ensemble size versus CPU time can become a problem when working
with large proteomes. In order to overcome this limitation a method is needed
to decrease the CPU time required for predictions without losing the ``wisdom of the crowd''.

%An ensemble of \textit{n} networks can be viewed as a multilayer network where the output from the individual networks are the last hidden layer, and the weights to the output layer is $ \frac{1}{n} $. %Dette er vist ikke helt rigtigt, så det skal nok skrives om...
%Since any multilayer network can be described by a network with only one hidden layer, it should be possible to make a single network that performs the functions of the entire ensemble. Such a network can in theory be trained by running computer generated sequences through the ensemble, and training a single network to predict as the ensemble does.
%/PROBABLY NOT NEEDED!

